Model Operations (ModelOps) is to focus primarily on the governance and life cycle management of a wide range of operationalized AI (Artificial Intelligence) and decision models. This includes knowledge graphs, optimization, rules, machine learning, agent-based models, and linguistics. Model Operations (ModelOps) is at the center of every organization’s enterprise AI approach, if you are yet to understand why it matters from now onward.
There are three core Model Operations (ModelOps) capabilities that organizations must have or need if they want to succeed with AI at scale identified:
A) For starters, Need to deploy and serve ML (Machine Learning) models.
B) ModelOps provides monitoring capabilities to ensure ML (Machine Learning) models don’t go off the rails.
C) ML (Machine Learning) lifecycles must be managed.
Why ModelOps?
Organizations are struggling with analytics operationalization because of their lack of a formal system which organizes resources through analytic, IT, and the organization. The data alone does not drive the business but decisions that are made and executed do. The decisions that impact the organizations are made every day, to make it simple, we are drawing in the context for execution of the decision that brings results. As most of the organization will be facing execution issues. Since analytically driven decisions are smarter choices, it helps one organization to make the right choices or decisions every time while making thousands or millions of them every day. This is made by integrating analytics into the decision-making processes. It includes the operationalization of scale analytics. To simplify it, it is business intelligence (BI) to assist effective decision making and modern application of it in the context.
E-SPIN Group in the enterprise ICT solution supply, consulting, project management, training and maintenance business for multinational corporations and government agencies across the region E-SPIN do business. Feel free to contact E-SPIN for your digital transformation initiative and project requirements. For instance to monitor the end to end of your ModelOps backed enterprise system together with the infrastructure to monitor for the availability, performance, security testing and continuous protection.
